Vehicle lane learning

ABSTRACT

A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One general aspect includes a system, including a computer having a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to receive an image of at least one lane marker from an image capture device mounted to a vehicle. The system also identifies a lane transition according to the image. The system can also control at least one of steering, braking, and acceleration of the vehicle according to a history of data concerning the lane transition locations.

BACKGROUND

Tracking lane markings is important for various kinds of driverassistance systems in modern motor vehicles. For example, a lanedeparture warning (LDW) can use the tracking of lane markings todetermine the position of the vehicle within the lane and can emit awarning signal if the vehicle gets too close to, or crosses, a laneboundary. However, mechanisms are lacking for vehicles to identify lanemarkings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary vehicle on a highway roadwaydetecting a left and a right roadway marking.

FIG. 2 is a detailed block diagram illustrating a portion of the highwayroadway of FIG. 1 including an exemplary vehicle detecting a change inthe left lane marking indicating a highway exit ramp.

FIG. 3 is an exemplary transition chart which can predict the vehicleresponse at certain locations.

FIG. 4 is an exemplary resultant action matrix which may represent oneof the cells of the exemplary transition chart of FIG. 3.

FIG. 5 is a flowchart of an exemplary process that may be implemented bythe vehicle's computer.

FIG. 6 is a flowchart of a second exemplary process that may beimplemented by the vehicle's computer.

DETAILED DESCRIPTION

Learning the Road

Referring to FIG. 1, illustrated is an exemplary vehicle lane markerdetection system 5 for a vehicle 10 on a highway lane 13, which forpurposes of this example is a highway such as a beltway around a city20. The vehicle 10 lane has a left lane marker 11, which is a singlebroken line (e.g., a conventional painted lane marking on a roadway) tothe left of the vehicle 10 and a single unbroken line at the right lanemarker 16, which changes to a second single broken line 19 at an exitramp 17. The vehicle 10 has one or more image capture devices, such asforward facing camera 12 with a left view site line 15 and a right viewsite line 14.

Now with reference to FIG. 2, which is a detail blocked diagram of thesystem 5 of FIG. 1 which better illustrates the left lane marker 11 tothe left of the vehicle 10 and the single unbroken line right lanemarker 16 to the right, which changes to the second single broken line19 at the beginning of the exit ramp 17. Also shown is a computer 8,which can also be referred to as an imaging electronic control unit(ECU). The computer 8 has at least one processor and memory to storecomputer instructions, register values, and temporary and permanentvariables. The instructions include one or more predetermined detectioncriteria for a lane marker.

The computer 8 may also include an additional special processor, such asan image processor or a digital signal processor (DSP) to aid theprocessor with signal interpretation. The computer 8 is communicativelycoupled to the camera 12, e.g., via a vehicle communications bus orother vehicle network such as is known.

As the vehicle 10 traverses around the highway lane 13, the camera 12can substantially continuously capture images of the highway lane 13ahead. The computer 8 substantially continuously receives images of thehighway and the right and left lane markers. The computer 8 includesprogram instructions to determine the occurrence of a detectedtransition identifier, for example, when the right lane marker 16 is asolid white line having a width of 50 cm and the left lane marker is adashed line having a width of 50 cm. Several data entries can berecorded into memory when the right lane marker 16 changes to a dashedline with a width of 50 cm, even if there was not a change in the leftlane marker 11. The entries can include, for example, a geolocation ofthe transition, a lane marker type of change, a change of direction ofthe vehicle 10, etc.

FIG. 3 is an example of a transition chart 25 which can be assembledfrom historical data as the vehicle 10 traverses the highway lane 13. A“transition,” as that term is used herein, encompasses an event in whicha vehicle 10 changes lanes. For example, when the vehicle 10 approachesa first exit ramp 17, as indicated in row 27 in the chart 25 as theintersection of I-85 and Main Street, the vehicle 10 exited the highwaylane 13 twenty-six times of the last fifty times the vehicle 10identified the exit ramp 17. However, when the vehicle 10 approachedsecond exit ramp 18, identified in row 21, which is at the intersectionof I-85 and Central Avenue, the vehicle 10 exited the highway lane 13five times of the last fifty times. Therefore, at each transitionposition, i.e., an instance in which the vehicle 10 traverses a portionof a highway that can be a subject of the chart 25, i.e., a place wherethe vehicle 10 could be exiting, changing lanes and/or turning, and isidentified by, for example, a change in the lane markings, can berepresented by the values of the cells in the transition chart 25. Thesevalues are incremented each time the vehicle 10 traverses a transitionposition. A “location column” 22 of the chart 25 identifies the locationof the transition position and a “times exited” column 23 has a numberof times the vehicle 10 exited, changed lanes or took some otheridentifiable action. A “total number of trips” column 24 has a runningtotal of the number of trips the vehicle has taken on a particular routeand an “earliest date” column 26 keeps track of an earliest date thevehicle 10 has come across the transition position indicated in thecolumn 22. The earliest date 26 can be used to keep the chart 25current, for example, any trips recorded that are more than a year oldcan be removed from the “times exited” column 23 and the “total numbertrips” column 24.

FIG. 4 is an example resultant action matrix 30 which can include anentry for each lane transition. A resultant action matrix 30 maps datarepresented in the aggregate in a transition chart 25, e.g., the timesexited column 23 from the chart 25 shown for the transition location ofthe row 27 is shown in detail in the matrix 30 of FIG. 4. Morespecifically, the action matrix 30 represents one of the cells of theexemplary transition chart 25 of FIG. 3 of vehicle 10 positions relativeto the left lane marker 11 for twenty-eight distance increments (indexes1 to 28 along the vertical axis) as the vehicle 10 approaches the exitramp 17. An index 0 is a location on the highway lane 13 designated as areference location for a transition location; the indexes may thenrepresent predetermined distance increments, e.g., 1 meter, 3 meters, 5meters, etc., along the highway lane 13 with reference to the 0 indexlocation. (For example, with 28 rows in the index of the matrix 30, atransition from one row to the next row represents approximately 3.5meters.) Values in each cell in the matrix 30 thus represent a number oftimes that a vehicle 10 had been at the lateral offset indicated by theleft lane offset shown on the vertical axis for each time that thevehicle 10 has passed the transition location's 0 index, the 0 positionfor the lateral offset being a leftmost border of a leftmost lane of thehighway lane 13. Thus, the matrix 30 provides a history of vehicle 10passages through the approach to the exit ramp 17, e.g., forty differenttrips in this example.

As stated above, each time a transition occurs, the appropriate cells ofthe resultant action matrix 30 are updated, for example, when thevehicle travels through the transition area, the cells representing thelateral and longitudinal positon at each of the longitudinal indexes 0to 28 will be incremented by one.

The datum in each row and column of the matrix 30 therefore provides anumber of times that a response to a lane marking was recorded at aparticular lateral position in the lane (i.e., at a particular distancefrom the left lane marker) at a particular distance index. Thus, overtime the resultant action matrix 30 will provide a history of traveleither through the transition location, where higher numbers represent ahigher probability of the vehicle 10 tendency to follow the matrixlearned path. For example, with reference to row 31 (at index 0), thevalue “2” is provided at the intersection of the lateral position 0 andlongitudinal index of 0, reading from left to right, where each adjacentbox represents a segment of the width of the lane of approximately equalto 20 cm (centimeters), which can total the width of the highway lane13, which in this example is approximately 3.2 meters. That is, thevehicle 10 determined that it was at the extreme left of the highwaylane 13 two times of the last forty passages through this location onthe highway lane 13. The vehicle was 100 cm from the left lane marker 11one time of the forty passages. The vehicle was 120 cm from the leftlane marker 11, one time. The vehicle was 140 cm from the left lanemarker 11 eight times and the vehicle 10 was 160 cm from the left lanemarker 11 thirteen times. Continuing, the vehicle 10 was 180 cm awaynine times, 200 cm away three times, 220 cm away one time, 280 cm awayone time and 300 cm away from the left lane marker 11 one time.

In the next row, row 32, with a longitudinal index of one, it can beseen that the vehicle 10 was 160 cm away from the left lane marker 11fourteen times, 180-220 cm away from the left lane marker 11 eight timesand 240-320 cm away three times. In the next row, row 34 (longitudinalindex 2), it can be seen that the vehicle was 160 cm away from the leftlane marker 11 fourteen times, 180-220 cm away from the left lane marker11 eleven times and 240-320 cm away two times. At a row 35 (longitudinalindex of 7), the vehicle 10 was 160 cm away from the left lane marker 11thirteen times, 180-220 cm away from the left lane marker 11 eleventimes and 240-320 cm away three times. At a row 36 (longitudinal indexof 9), the vehicle 10 was 160 cm away from the left lane marker 11eleven times, 180-220 cm away from the left lane marker 11 ten times and240-320 cm away six times. At a row 38 (longitudinal index of 11), thevehicle 10 was 160 cm away from the left lane marker 11 eleven times,180-220 cm away from the left lane marker 11 eight times and 240-320 cmaway eight times. At a row 40 (longitudinal index of 16), the vehicle 10was 160 cm away from the left lane marker 11 twelve times, 180-220 cmaway from the left lane marker 11 seven times and 240-320 cm away seventimes. FIG. 4 shows that the vehicle 10 tended to stay in the middle ofthe highway lane 13, however, the number of times the vehicle 10 exitedthe highway lane 13 is apparent by noting the number of times thevehicle 10 had entries in the 140-300 cm columns. An arrow 29 issuperimposed upon the center columns of FIG. 4 to represent the tendencyfor the vehicle 10 to stay in the middle of the highway lane 13. Asecond arrow 28 is representative of the occasional tendency when thevehicle 10 leaves the highway lane 13 and exits via the exit ramp 17.

When the computer 8 is detecting lane markers and lane makertransitions, the computer 8 can classify the lane markings into aninvalid lane category and a valid lane category. The valid lane categorycan include, for example, a single unbroken line, a double unbrokenline, a single broken line, a double broken line, a broken and unbrokenline, a wide broken line, a line with surface profile and a singleunbroken with single broken line. The invalid lane marker can be, forexample, a guide rail or a land mark.

The vehicle 10 position can be obtained via several methods including aglobal navigation satellite system (GNSS) or a Global Positioning System(GPS) receiver, a dead reckoning system, an inertial navigation systemand can be calculated using the number of tire rotations to determinethe distance from a known start reference point.

The GNSS is a system of satellites that provide autonomous geo-spatialpositioning with global coverage. It allows small electronic receiversto determine their location (longitude, latitude, andaltitude/elevation) to high precision (within a few meters) using timesignals transmitted along a line of sight by radio from satellites. Thesignals also allow the electronic receivers to calculate the currentlocal time to high precision, which allows time synchronization. GPS isUnited States of America term for a space-based navigation system thatprovides location and time information in all weather conditions,anywhere on or near the Earth where there is an unobstructed line ofsight to four or more GPS satellites.

Dead reckoning is the process of calculating one's current position byusing a previously determined position, or “fix”, and advancing thatposition based upon known or estimated speeds over elapsed time andcourse. The vehicle 10 would obtain a “fix” and calculate the directionand distance traveled for a certain time and determine the vehicle 10new location. The internal navigation system is course plotting aid thatuses a computer, motion sensors (accelerometers) and rotation sensors(gyroscopes) to continuously calculate via dead reckoning the position,orientation, and velocity (direction and speed of movement) of a movingobject without the need for external references.

The geolocation of the vehicle 10 can be in Universal TransverseMercator (UTM), coordinate system, a vehicle coordinate system asdefined by the International Organization for Standardization (ISO) fora vehicle coordinate system, a military grid reference system (MGRS) anda universal polar stereographic (UPS) system. The UTM system divides theEarth between 80° S and 84° N latitude into 60 zones, each 6° oflongitude in width. Zone 1 covers longitude 180° to 174° W; zonenumbering increases eastward to zone 60, which covers longitude 174° to180° E. Each of the 60 zones uses a transverse Mercator projection thatcan map a region of large north-south extent with low distortion. Byusing narrow zones of 6° of longitude (up to 800 km) in width, andreducing the scale factor along the central meridian to 0.9996 (areduction of 1:2500), the amount of distortion is held below 1 part in1,000 inside each zone.

The MGRS is the geocoordinate standard used by NATO militaries forlocating points on the earth. The MGRS is derived from the UniversalTransverse Mercator (UTM) grid system and the universal polarstereographic (UPS) grid system, but uses a different labelingconvention. The MGRS is used for the entire earth.

The UPS coordinate system is used in conjunction with the universaltransverse Mercator (UTM) coordinate system to locate positions on thesurface of the earth. Like the UTM coordinate system, the UPS coordinatesystem uses a metric-based Cartesian grid laid out on a conformallyprojected surface.

In addition, the path may be filtered into the driving path using knownKalman or other filtering techniques. Providing a Kalman filter, forexample, can compensate for noisy readings which can ‘jump around’rapidly, though always remaining within a few meters of the realposition. In addition, since the vehicle 10 is expected to follow thelaws of physics, its position can also be estimated by integrating itsvelocity over time, determined by keeping track of wheel revolutions andthe angle of the steering wheel. As discussed above, this is a techniqueknown as dead reckoning. Typically, the dead reckoning will provide avery smooth estimate of the vehicle 10 position, but it will drift overtime as small errors accumulate.

The Kalman filter can be thought of as operating in two distinct phases:predict and update. In the prediction phase, the vehicle 10 positionwill be modified according to the physical laws of motion (the dynamicor “state transition” model) plus any changes produced by theaccelerator pedal and steering wheel. A new position estimate can becalculated and inserted into the transition chart as well as an updateto the resultant action matrix.

In operation, the vehicle lane marker detection system 5 may erroneouslydetermine that the vehicle 10 is in traveling through a center median.Since it is physically impossible to travel through a solid, theerroneous positional determination will be treated as noise and theKalman filter can eliminate and/or suppress such spurious calculatedvehicle 10 positions. The Kalman filter can use coefficients based uponthe vehicle 10 travel history, for example, previous trips on thehighway lane 13.

A dead reckoning positional error of the vehicle 10 position is in part,proportional to the speed of the vehicle 10. This is due to theuncertainty about the accuracy of the dead reckoning position estimatesat higher speeds, as a small amount of positional errors grow rapidly athigher speeds than at slower speeds. Therefore, once the vehicle 10detects a “known position”, such as a lane marker, the system cancorrect for any dead reckoning drift form the actual position. Other“known positions,” for example, can be a lane marker transition, a lanemarker at a known intersection, a road sign, a land marks, etc.

Process Flows

FIG. 5 is a flow chart illustrating an exemplary process 100 of thecomputer 8 to capture an image of lane markings, determine the vehicle'srelative position in the lane and the vehicle's geolocation and save thevalues in a transition matrix.

The process 100 begins in a block 105, which can also follow in a block115 or in a block 125. The camera 12 captures a forward facing image(relative to the vehicle 10) of the highway lane 13. The image is storedin memory on the computer 8, which can also be known as an imaginingelectronic control unit (ECU), and the right and left lane marker typesare identified, e.g., using image recognition techniques such as areknown and that can be included in program instructions in the computer8. As discussed above, the lane marker types can include a singleunbroken line, a double unbroken line, a single broken line, a doublebroken line, a broken and unbroken line, a wide broken line, a line withsurface profile and a single unbroken with single broken line. Thecomputer 8 can also usually differentiate an invalid image object from alane marking, for example, the computer 8 can determine that the lanemarker is not a lane marker, but rather a guard rail.

In a block 110, a counter is incremented to a next position dicating animage and its characteristics have been loaded into memory. Thecharacteristics can include the right and left lane marker types and thegeolocation of the vehicle 10.

Next, in the block 115, the computer 8 determines if the image stored ina most recent iteration of the block 105 is a first image captured, andif it is the first image captured, the system will return to in theblock 105 and capture a next sequential image, else the system 100 willcontinue in a block 120.

Next, in a block 120, the current image characteristics are compared tothe previous image's characteristics, for example, the computer 8determines that the current right lane marker has changed from a singleunbroken to a single broken line. If there is a difference in imagecharacteristics, the process 100 continues in a block 125, else theprocess returns to the block 105.

Next, in a block 130, the system 100 determines a lane offset distanceof the vehicle 10 with respect to the lane the vehicle 10 is in, forexample, if the vehicle 10 is in the center of the highway lane 13 andthe lane is three meters wide, the left lane marker offset can be 150 cmto the center of the vehicle 10. Additionally, the vehicle 10geolocation can be determined from the methods cited above, including aglobal navigation satellite system (GNSS) or a Global Positioning System(GPS) receiver, a dead reckoning system, an inertial navigation systemand can be calculated using the number of tire rotations to determinethe distance from a known start reference point.

Next, a block 135, the computer stores the left lane marker offset, theleft lane marker type, the right lane marker type and a geolocation ofthe vehicle 10 into a memory.

Next, in a block 140, the computer 8 determines if the segment of thetrip requiring collecting images and lane marking data is complete, andif it is the process 100 ends, else the process 100 returns to the block105.

FIG. 6 is a flow chart illustrating an exemplary process 200 of thecomputer 8 for determining the location of the vehicle 10 and an exitramp.

The process 200 begins in a block 205, which can also follow in a block220 or in a block 240. The camera 12 captures a forward facing image(relative to the vehicle 10) of the highway lane 13. The image is storedin memory on the computer 8.

Next in a block 210, the computer 8 determines the position of thevehicle 10. The position can be generally determined using GNSS or deadreckoning from a known start point

Next, in a block 215, the processor compares the captured imagecharacteristics with known geolocations and their characteristics. Forexample, when the right lane marker 16 changes from the single unbrokenline to the second single broken line 19 at the exit ramp 17. Thecomputer 8 can then determine the vehicle 10 position on the highwaylane 13.

Next, in the block 220, the computer 8 makes a determination in any ofthe recently captured image's characteristics matches anycharacteristics of previously stored images in the transition matrix. Ifthere is a match, the process continues to in a block 225, else theprocess returns to in the block 205 to capture and process another imagefrom the camera 12.

Next, in the block 225, the process 200 can optionally capture anotherimage from the camera 12 and its lane marking characteristics areextracted.

Next in a block 230, the optional image lane characteristics from in theblock 225 are checked against the database to verify the positioning ofthe vehicle 10.

Next, profile in a block 235, the computer 8 sends a control signal tothe vehicle 10 to commence the egress of the highway lane 13 onto theexit ramp 17. If the vehicle 10 is an autonomous vehicle, the vehicle'sonboard control and navigation system will maneuver the vehicle bycontrolling one or more of steering, braking, and acceleration. If thevehicle is a non-autonomous vehicle, the computer 8 will send an alertto the vehicle 10 and the operator that the vehicle 10 is approaching adesired exit.

In other words, if the exit and the highway path have been traveledrepeatedly, then there will be a statistical preference of which path isa preferred path and its preferred shape of travel relative to theresultant action matrix 30, starting at the transition point of theparticular transition matrix cell. When a detection of a particulartransition is detected then driver can be alerted of the preferredlearned decision and take action unless canceled by the driver orpassenger.

Next in a block 240, the computer 8 verifies that the vehicle 10 is onthe exit ramp. This can be accomplished with another image capture ofthe lane markings or by taking a GNSS position. If the vehicle is on theexit ramp 17, the process continues to in a block 250, else the processreturns to in the block 205.

Next, in a block 250, the computer 8 sends a message to the vehicle'sonboard control and navigation system confirm the egress or a message tothe operator. Following the block 250, the process 200 ends.

CONCLUSION

As used herein, the adverb “substantially” modifying an adjective meansthat a shape, structure, measurement, value, calculation, etc. maydeviate from an exact described geometry, distance, measurement, value,calculation, etc., because of imperfections in the materials, machining,manufacturing, sensor measurements, computations, processing time,communications time, etc.

Computing devices such as those discussed herein generally each includeinstructions executable by one or more computing devices such as thoseidentified above, and for carrying out blocks or steps of processesdescribed above. Computer executable instructions may be compiled orinterpreted from computer programs created using a variety ofprogramming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Java™, C, C++, C#,Visual Basic, Python, Java Script, Perl, HTML, PHP, etc. In general, aprocessor (e.g., a microprocessor) receives instructions, e.g., from amemory, a computer readable medium, etc., and executes theseinstructions, thereby performing one or more processes, including one ormore of the processes described herein. Such instructions and other datamay be stored and transmitted using a variety of computer readablemedia. A file in a computing device is generally a collection of datastored on a computer readable medium, such as a storage medium, a randomaccess memory, etc.

A computer readable medium includes any medium that participates inproviding data (e.g., instructions), which may be read by a computer.Such a medium may take many forms, including, but not limited to,non-volatile media, volatile media, etc. Non-volatile media include, forexample, optical or magnetic disks and other persistent memory. Volatilemedia include dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Common forms of computer readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any othermemory chip or cartridge, or any other medium from which a computer canread.

With regard to the media, processes, systems, methods, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. In other words, the descriptions ofsystems and/or processes herein are provided for the purpose ofillustrating certain embodiments, and should in no way be construed soas to limit the disclosed subject matter.

Accordingly, it is to be understood that the above description isintended to be illustrative and not restrictive. Many embodiments andapplications other than the examples provided would be apparent to thoseof skill in the art upon reading the above description. The scope of theinvention should be determined, not with reference to the abovedescription, but should instead be determined with reference to claimsappended hereto and/or included in a non-provisional patent applicationbased hereon, along with the full scope of equivalents to which suchclaims are entitled. It is anticipated and intended that futuredevelopments will occur in the arts discussed herein, and that thedisclosed systems and methods will be incorporated into such futureembodiments. In sum, it should be understood that the disclosed subjectmatter is capable of modification and variation.

What is claimed is:
 1. A system, comprising a computer having aprocessor and a memory, the memory storing instructions executable bythe processor such that the computer is programmed to: receive an imageof at least one lane marker from an image capture device mounted to avehicle; identify a lane transition location according to the image; andcontrol at least one of steering, braking, and acceleration of thevehicle according to a history of data concerning the lane transitionlocation.
 2. The system of claim 1, wherein the computer is furtherprogrammed to determine an offset distance from a lane marker to thevehicle.
 3. The system of claim 2, wherein the computer is furtherprogrammed to: determine a geolocation of the vehicle; and store in thememory at least a lane marker type, a pattern of the lane marking, theoffset distance and the geolocation of the vehicle.
 4. The system ofclaim 1, wherein the computer is further instructed to assign a lanemarker to a lane marking category.
 5. The system of claim 4, wherein thelane marking category includes at least a valid lane marker or aninvalid lane marker.
 6. The system of claim 5, wherein the computerfurther determines the invalid lane marker when the lane marker type isan invalid image object.
 7. The system of claim 1 wherein the lanemarker includes at least a single unbroken line, a double unbroken line,a single broken line, a double broken line, a broken and unbroken line,a wide broken line, and a single unbroken with single broken.
 8. Thesystem of claim 2, wherein the computer further determines a change inthe lane marker and store in the memory at least the lane marker type,the pattern of the lane marking, the offset distance and the geolocationof the vehicle.
 9. The system of claim 8, wherein the computer isfurther instructed to obtain the geolocation from at least a GlobalPositioning System (GPS) receiver, a dead reckoning system; and aninertial navigation system.
 10. The system of claim 3, wherein thegeolocation is at least a Universal Transverse Mercator (UTM) coordinatesystem, a military grid reference system (MGRS) and a universal polarstereographic (UPS) system.
 11. A method, comprising: receiving an imageof at least one lane marker from an image capture device mounted to avehicle; confirming that the image meets a predetermined detectioncriterion as a detected lane marking; determining a lane marker type;determining if a pattern of the lane marking has changed from a previousimage; comparing the pattern of the lane marker to a set of geolocationsand their associated characteristics that are stored in a memory; andcontrolling at least steering, braking, acceleration of the vehicle asthe vehicle egresses a highway.
 12. The method of claim 11, furthercomprising determining an offset distance from the lane marker to thevehicle.
 13. The method of claim 12, further comprising: determining ageolocation of the vehicle; and storing in a memory at least the lanemarker type, the pattern of the lane marking, an offset distance and thegeolocation of the vehicle.
 14. The method of claim 11, furthercomprising assigning the lane marker to a lane marking category.
 15. Themethod of claim 14, wherein the lane marking category includes at leasta valid lane marker or an invalid lane marker.
 16. The method of claim15, further comprising determining the invalid lane marker from aninvalid image object.
 17. The method of claim 13 wherein the lane markerincludes at least a single unbroken line, a double unbroken line, asingle broken line, a double broken line, a broken and unbroken line, awide broken line, and a single unbroken with single broken.
 18. Themethod of claim 12, further comprising: determining a change in the lanemarker; and storing in memory at least at least the lane marker type,the pattern of the lane marking, the offset distance and the geolocationof the vehicle.
 19. The method of claim 18, further comprisingdetermining to obtain the geolocation from at least a Global PositioningSystem (GPS) receiver, a dead reckoning system; and an inertialnavigation system.
 20. The method of claim 13, wherein the geolocationis at least a Universal Transverse Mercator (UTM) coordinate system, amilitary grid reference system (MGRS) and a universal polarstereographic (UPS) system.